Airborne Particle Classification with a Combination of Chemical Composition and Shape Index Utilizing an Adaptive Resonance Artificial Neural Network

1994 ◽  
Vol 28 (11) ◽  
pp. 1921-1928 ◽  
Author(s):  
Ying. Xie ◽  
Philip K. Hopke ◽  
Dietrich. Wienke
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Composition ◽  
Shape Index ◽  
Airborne Particle ◽  
Adaptive Resonance ◽  
Artificial Neural ◽  
Particle Classification

Detection of malware using an artificial neural network based on adaptive resonant theory

2021 ◽  
pp. 69-82
Author(s):  
D. G. Bukhanov ◽  
◽  
V. M. Polyakov ◽  
M. A. Redkina ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Significant Loss ◽  
Malicious Code ◽  
Resonance Theory ◽  
Similarity Parameter ◽  
Adaptive Resonance ◽  
Artificial Neural ◽  
And Behavior ◽  
Classification Time

The process of detecting malicious code by anti-virus systems is considered. The main part of this process is the procedure for analyzing a file or process. Artificial neural networks based on the adaptive-resonance theory are proposed to use as a method of analysis. The graph2vec vectorization algorithm is used to represent the analyzed program codes in numerical format. Despite the fact that the use of this vectorization method ignores the semantic relationships between the sequence of executable commands, it allows to reduce the analysis time without significant loss of accuracy. The use of an artificial neural network ART-2m with a hierarchical memory structure made it possible to reduce the classification time for a malicious file. Reducing the classification time allows to set more memory levels and increase the similarity parameter, which leads to an improved classification quality. Experiments show that with this approach to detecting malicious software, similar files can be recognized by both size and behavior.

PASSPHRASE AUTHENTICATION BASED ON TYPING STYLE THROUGH AN ART 2 NEURAL NETWORK

2002 ◽  
Vol 02 (02) ◽  
pp. 131-152 ◽  
Author(s):  
JASON BECHTEL ◽  
GURSEL SERPEN ◽  
MARCUS BROWN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Authentic Sample ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Algorithm

This study proposes the use of an artificial neural network algorithm to perform passphrase authentication based on the typing style of a user. The only hardware required is a keyboard. Prior studies have demonstrated the feasibility of this approach and its limitations, one of which was the need for collection of impostor samples for training the artificial neural network based classifier algorithm. This requirement is rather impractical for most application domains. The proposed study eliminates the need to collect impostor samples by employing an unsupervised and self-organizing artificial neural network algorithm, the Adaptive Resonance Theory 2 neural network, and therefore pushes the passphrase authentication technology one step closer to the realm of practical implementation. The preliminary study performed demonstrates that it is possible to train an Adaptive Resonance Theory 2 neural network using only authentic sample data and still provide a relatively low impostor pass rate. Given the minimal cost and easy in-field trainability of the proposed passphrase authentication system, the developed system can greatly enhance the security of computing environments with wide acceptance.

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